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The commercial contribution of clinical studies for pharmaceutical drugs
Authors:Ashish Sood  Eelco Kappe  Stefan Stremersch
Affiliation:1. Wharton School of Business, University of Pennsylvania, United States;2. Smeal College of Business, Pennsylvania State University, University Park, PA 16802, United States;3. Erasmus School of Economics, Erasmus University Rotterdam, Burgemeester Oudlaan 50, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands;4. IESE Business School, Universidad de Navarra, Spain
Abstract:Pharmaceutical drugs are rigorously evaluated through clinical studies. The commercial consequences of such clinical studies, both to the promotion for and sales of drugs, are largely under-researched. The present study answers the following research questions: 1) How does the evolution of clinical study outcomes affect product sales? 2) How does the evolution of clinical study outcomes affect a firm's promotion expenditures to physicians and consumers? 3) Is the assessment of the responsiveness of sales to promotion expenditures biased when the analyst omits the role of clinical studies? We summarize a comprehensive body of clinical studies in three metrics: valence, dispersion, and volume. We extend the literature with the following findings. A higher valence and volume of clinical studies (i.e., more positive and larger number of studies) increase sales. A higher valence of clinical studies increases spending on both direct-to-consumer advertising and direct-to-physician promotion. A higher dispersion among clinical studies decreases spending on direct-to-consumer advertising. A higher volume of clinical studies has no effect on direct-to-physician promotion, but decreases direct-to-consumer advertising. Furthermore, the results show that omitting these metrics from a market response model leads to an overestimation of the responsiveness of sales to promotion expenditures.
Keywords:Pharmaceutical marketing   DTP (direct-to-physician promotion)   DTCA (direct-to-consumer-advertising)   Reviews   Clinical studies   Time series model
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